RANCANG BANGUN SISTEM IRIGASI OTOMATIS BERBASIS IOT DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK OPTIMALISASI PENGELOLAAN AIR PADA PERTANIAN PADI

    Rizal Ramdhani, - and Diky Zakaria, - and Mahmudah Salwa Gianti, - (2025) RANCANG BANGUN SISTEM IRIGASI OTOMATIS BERBASIS IOT DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK OPTIMALISASI PENGELOLAAN AIR PADA PERTANIAN PADI. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    Pertanian padi di Indonesia, khususnya di wilayah Indramayu, menghadapi tantangan besar akibat kekeringan yang dipicu oleh perubahan iklim, sehingga diperlukan inovasi sistem irigasi yang efisien dan adaptif. Penelitian ini bertujuan merancang dan membangun prototipe sistem irigasi otomatis berbasis Internet of Things (IoT) dengan algoritma Particle Swarm Optimization (PSO) untuk mengoptimalkan penggunaan air. Sistem ini terdiri dari sensor kelembapan tanah, sensor ketinggian air, dan flow sensor yang terhubung ke mikrokontroler ESP32, di mana data diproses secara lokal untuk menentukan volume dan durasi penyiraman optimal. Proses pengembangan mengikuti model ADDIE, mulai dari analisis kebutuhan hingga evaluasi performa sistem. Data dikirim ke Firebase dan dimonitor melalui aplikasi Android berbasis MIT App Inventor. Hasil pengujian menunjukkan bahwa sistem ini mampu menghemat penggunaan air hingga 55,31% dibandingkan irigasi konvensional. PSO terbukti efektif dalam menemukan solusi optimal secara cepat dan adaptif terhadap perubahan lingkungan. Dengan kemampuan monitoring dan kontrol jarak jauh, sistem ini mendukung praktik pertanian yang efisien dan berkelanjutan, serta berpotensi menjadi solusi nyata dalam menghadapi krisis air di sektor pertanian padi. Inovasi ini memberikan kontribusi nyata dalam transformasi digital pertanian modern. ----- Rice farming in Indonesia, particularly in Indramayu, is facing major challenges due to drought triggered by climate change, requiring innovative, efficient, and adaptive irrigation systems. This study aims to design and develop a prototype of an Internet of Things (IoT)-based automatic irrigation system optimized with the Particle Swarm Optimization (PSO) algorithm to improve water usage efficiency. The system integrates soil moisture sensors, water level sensors, and flow sensors connected to an ESP32 microcontroller, which processes data locally to determine optimal watering volume and duration. The development process follows the ADDIE model, covering needs analysis to system performance evaluation. Data is transmitted to Firebase and monitored via an Android application built with MIT App Inventor. Testing results show that the system can save up to 55.31% of water compared to conventional irrigation methods. PSO effectively finds optimal solutions quickly and adapts to environmental changes. With real-time monitoring and remote control capabilities, this system promotes efficient and sustainable farming practices and offers a practical solution to water scarcity in rice agriculture. This innovation contributes significantly to the digital transformation of modern agriculture.

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    Official URL: https://repository.upi.edu/
    Item Type: Thesis (S1)
    Additional Information: https://scholar.google.com/citations?hl=id&user=i2X0uV0AAAAJ&scilu=&scisig=ACUpqDcAAAAAaLUuf8AzkBgec2B5d_DoPGj7Rng&gmla=AH8HC4yRf0sq0vf1vTsDlP0tZMnUryQaFwgQO_MMoCMN8vJSzpcRyRM1Zy_JQDxh4Cwadt_GwgLf1Bc5ig7rSJSv1mwTebVTfwUlhN0pcvOup4NWXo4p2K2j1iw&sciund=10444646270909685258 ID SINTA Dosen Pembimbing: Diky Zakaria: 6779007 Mahmudah Salwa Gianti: 6779018
    Uncontrolled Keywords: Irigasi otomatis, IoT, Particle Swarm Optimization, efisiensi air, pertanian padi. Automatic irrigation, IoT, Particle Swarm Optimization, water efficiency, rice farming.
    Subjects: L Education > L Education (General)
    T Technology > T Technology (General)
    T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: UPI Kampus Purwakarta > S1 Mekatronika dan Kecerdasan Buatan
    Depositing User: Rizal Ramdhani
    Date Deposited: 01 Sep 2025 08:52
    Last Modified: 01 Sep 2025 08:52
    URI: http://repository.upi.edu/id/eprint/137175

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